May 30, 2023 · This letter proposes an unsupervised method based on 3-D depthwise separable convolutional autoencoders (DSConvAEs).

The change detection for hy- perspectral images (HSI-CD) is to identify the variations in geographic areas among two or multiple temporal images from the same ...

The former utilizes depthwise separable convolutions and attention mechanisms to emphasize the features of dual-temporal HSIs from the spatial perspective.

An attention mechanism and depthwise separable convolution are introduced to the three-dimensional convolutional neural network (3DCNN).

Oct 22, 2024 · A method called S3DCAE [9] uses a three dimensional deep convolutional autoencoder to extract relevant features from the hyperspectral images.

Jul 2, 2024 · The major components of the proposed framework are standard 3D-CNN, pseudo-3D block, depthwise separable 3D CNN, depth separable 2D CNN, and ...

Jun 10, 2020 · Deep learning (DL) algorithms are considered as a methodology of choice for remote-sensing image analysis over the past few years.

A Multi-Scale Depthwise Separable Capsule Network (MDSC-Net) is proposed in this article for HSI classification.

The major components of the proposed framework are standard 3D-CNN, pseudo-3D block, depthwise separable 3D CNN, depth separable 2D CNN, and spatial pyramid ...

Depthwise Separable Convolutional Autoencoders for Hyperspectral Image Change Detection. Request PDF. Restricted access. IEEE Geoscience and Remote Sensing ...